Investigating a Participatory Intervention in Multidisciplinary Cancer Care Teams Using an Integrative Organizational Model: A Study Protocol
Bibliographic record
Abstract
Multidisciplinary teams encounter many challenges that can lead to higher levels of distress and burnout. This trend is acutely prevalent among multidisciplinary cancer care teams who frequently contend with increased task complexity and numbers of patients. Resilience is emerging as a critical resource that may optimize team members’ psychological health and wellbeing, work efficiency, and organizational agility, while reducing burnout. Accordingly, the proposed study aims to implement and evaluate a promising participatory interventional approach that fosters team resilience. Specifically, the effects of the intervention on participating team members will be compared to a control group of non-participating team members. This intervention’s core components include skills training, patient-centered meetings, talking spaces, and an agile problem-solving approach. The proposed study also seeks to determine whether enhanced resilience improves team mental health status and organizational outcomes. A participatory interventional approach will be implemented and assessed at three-time intervals [i.e., pre-intervention deployment (N = 375), 12 months post-deployment (N = 236), and 24 months post-deployment (N = 146)] across five cancer care teams in three Quebec healthcare institutions. A mixed methods design will be used that includes observations, semi-structured interviews, focus groups, and self-report questionnaires. Direct observation will document team functioning and structural resources (e.g., meetings, conflict management, and leadership). Semi-structured interviews will explore participants’ experience with activities related to the participatory interventional approach, its perceived benefits and potential challenges. Focus groups will explore participants’ perceptions of their team’s resilience and the effectiveness of the intervention. Questionnaires will assess support, recognition, empowerment, organizational justice, individual resilience, psychological safety, work climate, team resilience, workplace burnout, engagement, quality of work life, wellbeing, and organizational citizenship behaviors, and sociodemographic variables. Moreover, objective measures including absenteeism and staff turnover will be obtainedviahuman resource records. Structural equation modeling will be used to test the study’s hypotheses. The proposed protocol and related findings will provide stakeholders with quantitative and qualitative data concerning a participatory interventional approach to optimize team effectiveness. It will also identify critical factors implicated in favorable organizational outcomes in connection with multidisciplinary cancer care teams. Expected results and future directions are also presented herein.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".